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Designed, built, and proven on your real data. Then handed to your team on solid ground.
The reframe
I'm an AI systems architect with expertise in experience design. When AI goes into your operation, the job isn't shipping an interface — it's shipping something that brings value to customers, survives first contact with real people and live data, and keeps working after.
That value lives under the screen, in the exchange between you and your customer. Get that right, and the interface almost designs itself.
And I don't design it, then toss it over the wall to find out where it breaks. AI-native means one mind carries the whole thing from the real problem to a running system. No spec thrown to a separate build team, so nothing gets lost between the plan and what ships.
Architect, because it's never just one system: the human workflows, the agentic workflows, the data, the decisions, and where each one hands off. I design how they fit together, then build them into something that holds.
The scope
What your customer is hiring you for.
Before anything gets built, I find the job your customer is actually trying to get done, and where today's experience fails them. That's where the value gets defined, so the system solves the real problem instead of a clever one.
What makes them say yes.
I design for the moment a customer decides you're worth it, not just the demo that comes before it. That decision is where the value is won, so the system earns the yes instead of staging it.
How it reaches the people it's for.
From day one I design how it reaches people: the channels, the launch, the loops that compound. Reach isn't bolted on at the end, so what works in the room works in the market.
What it costs you to run.
I scope the system to the value it creates and what it costs to deliver, not to a feature wish list. That's how it earns its keep, and how the engagement stays sized to what's actually at stake.
Proof it holds.
I build it against your live data and measure it with evals tied to your business rules. Because if you can't measure the value, you can't trust it, and neither can your team.
Concretely, that's everything from discovery to evals:
Snowball Sprint
Storyboard
The human in the right place.
Most AI breaks at the seam, not the model. I storyboard the workflow to find where a person stays in the loop and where the AI takes over. That placement decides whether it works.
Digital Twin
Working systems, not wireframes.
I prototype in real code against your real data from day one. You see how the system actually behaves, and it breaks early, while breaking is still cheap.
Value Matrix
Proven before it's built.
I pressure-test ROI and risk with working prototypes before anyone writes production code, so the build stands on proven ground, not a guess.
Then comes the build. Once the Snowball Sprint proves it, I take it into production with your team so what ships is real, and they can run and extend it.
What I need from you.
Access to the people accountable.
Time on the calendar.
The authority to make calls.
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What you keep
The method your team owns and runs, not a dependency on me.
The evals, guardrails, and bar your AI is held to.
The people, not just the tech — skeptics and silos turned toward one direction.
An AI systems architect designs and builds the entire system around an AI, not just the model or the interface. That means the human workflows, the agentic workflows, the data, and the decisions, and how they all fit together. Ray Butler works AI-native: one mind carries a project from the real problem to a running system, designed and built in the same hands.
Not a recommendation deck — running against your real data, with the evals to prove it holds.
Let's talk